UCLA

Most analyses in the past three decades concerned estimating effects of causes (EoC). Less emphasis has been placed on identifying causes of effects (CoE), despite their critical importance in science, medicine, public policy, legal reasoning, AI, and epidemiology. For example, personalized medicine concerns the probability of a drug being the cause of survival: resulting in a favorable outcome if taken and unfavorable if avoided. One reason for this imbalance is that tools for estimating the probability of causation from data require counterfactual logic. Bounds on these probabilities are often too loose to be informative and the assumptions necessary for point estimates are often too strong to be defensible. The objective of this thesis is to develop and test techniques for achieving narrower bounds on the probabilities of causation, with minimal assumptions. These more accurate estimates are achieved by incorporating a causal model and covariate data.